What is Data Cleansing?

Data cleansing is the procedure of detecting corrupt data and removing them from the datasets. It also includes eliminating inaccurate, incorrect, and incomplete data.  It has been estimated that around 80-90% of data scientists time is spent for data cleansing. Use the checklist curated by us to mitigate quality issues with your data.

Data Cleansing Checklist:

List of factors that are considered in creating a successful data cleansing checklist:

1. Latest Data

Data should be up to date to provide optimal value from data analysis.

2. Duplicate Data

Duplicate data indicate multiple records from one client.

3. Check IDs

Check the data labels of all the files to see whether some definite value is mislabelled.

4. Missing Values

Count missing values and places where the data are missing. Missing values can disrupt some analysis and skew the results.

5. Numerical Outliners

Numerical outliners are relatively easy to detect and remove. Define minimum and maximum to spot the outliners.

6. Define Valid Output

Define valid data labels for categorical data. Defining data ranges for numerical variables and non-matching data is wrong.

Striking Statistics about “Data” Prioritizing the Value of Data Cleansing Checklist:

• 2.1% of the B2B data degrades every month
• The world produces 2.5 quintillion bytes of data daily.
• 7 out of 10 leading marketers ascertain the fact they depend upon data to keep up with decision-making at all levels.
• 43% of marketers integrate data across platforms through data-driven marketing.
• Marketers use personalization 83% of the time so they can outperform the revenue goals.
• 49% of the marketers have expressed the need to use data in the current strategy.
• 87% of the marketers brought to notice that data is the least-utilized asset.

Benefits of Data Cleansing:

There are many benefits of data cleansing, including improved decision making as well as better understanding by the client (or else the quality of the data diminishes at an alarming rate). Let’s look at this in more detail

1. Boosts Results and Revenue

Increased amount of ROI on communication campaigns and marketing is generated through clean data.

2. Save Money and Reduce Waste

Reducing the amount of waste and money spent is possible in physical marketing campaigns with an updated data list.

3. Save Time and Increase Productivity

Accurate data reduces the time wasted on invalid prospects.

4. Protect Reputation

Communications reach only to interested prospects who will benefit from the product/service.

5. Minimize Compliance Risks

Maintaining an updated dataset assists in staying away from humungous fines associated with breaching GDPR and other legislation.

6. Data Cleansing is Quick and Easy

Swift cleansing is possible as the process is extremely fast and straightforward. One hundred thousand data entries can be checked every 30 minutes.

Fourteen Data Cleaning Tools:

To adhere to the data cleansing checklist, tools are necessary to spot errors in the data sets.

Let’s look at a list of few of those tools:

• Programming Languages
• Microsoft Excel
• Data Visualizations
• Proprietary software
• Microsoft DQS
• TIBCO Clarity
• Tableau Prep
• Trifacta
• Cloudingo
• SAS Data Quality
• Oracle Enterprise Data Quality
• IBM Infosphere Information Server
• RingLead
• Drake

How to Evaluate Data Post Application of Data Cleansing Checklist:

Data still needs to be evaluated after data cleansing checklist parameters are applied. Here is how you do it.

1. Collect Comprehensive Data

A database providing data based on the numerous data selects makes B2B businesses more aware of their prospects.

2. Organize the Data Correctly

Once the data is segmented into insightful categories, businesses can acquire a hyper-targeting approach for their clients.

3. Eliminate the Excess

Rechecking it all over again doesn’t ensure that the data doesn’t carry anything which isn’t weighing in the analysis.

4. Median Calculations > Mean Calculations

The median is the middle value of your data, compared to the mean of the data, which is the average.

5. Get the Right Tech

Prioritize maintaining your data hygiene and identify gaps and weak spots before they harm your success.

Challenges Data Scientists Have to Deal With While Data Cleansing Preparation:

• Errors in domain format
• Missing values
• Violations in integrity constraints
• Embedded values
• Inconsistent data that creates confusion
• Incorrect data that could lead to bad decision-making and also affect client records
• Value entered in the wrong field
• Lexical errors

Conclusion

Laying the foundation of a proper data cleansing checklist can conserve organizations’ money, time, compliance, and security risks. This assists your organization to be more productive and efficient.